While most practice implements channel pruning by selecting channels based on rule-of-thumb designs, the notion of evaluating the class-discriminative information has recently fostered the channel pruning [1]}.
In particular, class-discriminative channel pruning has two unique advantages for federated unlearning: It can (1) find channels that have great separability with respect to the target categories, which fits seamlessly with the goal of classification forgetting; (2) make the unlearning more explainable, as it directly assesses what the channels have learned and quantizes the information of the channels.
However, recent works of channel pruning with class-discriminative metrics have not well studied the computational effectiveness.
For example, the recent work [2]} uses a closed-form function and the work [1]} inserts a cross-entropy type loss.
They both require iterative optimization steps or heavy matrix operation like matrix inversion to obtain the discrimination score, which can hinder the techniques to be applied to the large-scale distributed FL settings.
